The first purpose of this thesis is to provide a framework for the inclusion of data from a secondary source in Bayesian decision analysis as an aid in decision making under uncertainty. A second purpose is to show that the Bayesian procedures can be implemented on a computer to obtain accurate results at little expense in computing time. The state variables of a bridge design example problem are the unknown parameters of the probability distribution of the primary data. The primary source is the annual peak flow data for the stream being spanned. Information pertinent to the choice of bridge design is contained in rainfall data from gauges on the watershed but the distribution of this secondary data cannot be directly expressed in terms of the state variables. This study shows that a linear regression equation relating the primary and secondary data provides a means of using secondary data for finding the Bayes risk and expected opportunity loss associated with any particular bridge design and single new rainfall observation. The numerical results for the example problem indicate that the information gained from the rainfall data reduces the Bayes risk and expected opportunity loss and allows for a more economical structural design. Furthermore, the careful choice of the numerical methods employed reduces the computation time for these quantities to a level acceptable to any budget.

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Project Completion Report / OWRT Project No. A-027-ARIZ / Development of a mathematical model of infiltration which includes the effects of raindrop impact / Agreement No. 14-31-0001-3503 / Project Dates: July 1971-December 1972. / The purpose of this investigation was to use an existing mathematical model of infiltration to assist in determining which factors, including raindrop impaction, were responsible for infiltration characteristics of a bare semiarid watershed. The infiltration model developed by Roger Smith was selected as best suited for this investigation. Several laboratory and field rainfall simulator runs were modeled. Good correlation was found between the modeled and experimental results for both the infiltration data and the saturation profiles, for both bare and grass covered plots. For the lab and field experiments a realistic rotating disk rainfall simulator was used. In the field tests bare and grass covered plots were tested. In the lab specially constructed soil boxes were used that permitted measurement of infiltration and saturation profiles with time. Gross changes in saturated hydraulic conductivities due to crusting effects were also measured. Gamma ray attenuation techniques were used to obtain density and soil moisture profiles for the laboratory experiments. It was found that the Smith model can be used to simulate infiltration from different surface conditions as long as there is some method to calibrate the model. Carefully obtained saturated and unsaturated hydraulic properties for the soil types present in the watershed are needed in addition to infiltration data from a realistic rainfall simulator or through hydrograph analysis from unit subwatersheds.

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Distributed rainfall-runoff models are gaining widespread acceptance; yet, a fundamental issue that must be addressed by all users of these models is definition of an acceptable level of watershed discretization (geometric model complexity). The level of geometric model complexity is a function of basin and climatic scales as well as the availability of input and verification data. Equilibrium discharge storage is employed to develop a quantitative methodology to define a level of geometric model complexity commensurate with a specified level of model performance. Equilibrium storage ratios are used to define the transition from overland to channel-dominated flow response. The methodology is tested on four subcatchments in the USDA-ARS Walnut Gulch Experimental Watershed in southeastern Arizona. The catchments cover a range of basins scales of over three orders of magnitude. This enabled a unique assessment of watershed response behavior as a function of basin scale. High quality, distributed, rainfall-runoff data were used to verify the model (KINEROSR). Excellent calibration and verification results provided confidence in subsequent model interpretations regarding watershed response behavior. An average elementary channel support area of roughly 15% of the total basin area is shown to provide a watershed discretization level that maintains model performance for basins ranging in size from 1.5 to 631 hectares. Detailed examination of infiltration, including the role and impacts of incorporating small-scale infiltration variability in a distribution sense, into KINEROSR, over a range of soils and climatic scales was also addressed. The impacts of infiltration and channel losses on runoff response increase with increasing watershed scale as the relative influence of storms is diminished in a semi-arid environment such as Walnut Gulch. In this semi-arid environment, characterized by ephemeral streams, watershed runoff response does not become more linear with increasing watershed scale but appears to become more nonlinear.

Increasing populations in urban regions have prompted the development of areas previously undisturbed. This development has spurred the formation of numerous models and methods to simulate the effects of urbanization on runoff processes. The engineer who must use these models and methods needs to be aware of their capabilities and performance. Many of the models assume that calibration will take place to improve the final results. Unfortunately, this is not the case for the majority of drainage studies. A qualitative and quantitative evaluation is made to help the engineer decide which model or method is applicable in certain situations. Simulations are performed on eight watersheds in northern Virginia. Nineteen models are evaluated and compared to gaged events as well as calibrated design storms. The models include EPA SWMM, PSRM-QUAL, TR-20, HEC-1, TR-55, variations of the rational method, three-unit hydrograph procedures, the USGS regression equations, and the Anderson method. Coverage is given for all of the models to outline their capabilities. Hydrographs are evaluated with respect to peak flow, time to peak flow, time base, volume, and overall shape. Statistical measures are introduced to quantitatively test the modeled hydrograph to a baseline reference hydrograph. The statistics yield many errors with the models being evaluated. A selection criteria is given where the models may be chosen based on their performance. The table is limited to the range of watersheds evaluated. Trends in each model toward basin area, land use condition, and general model type are discussed. A cross-calibration technique for improving the accuracy of some models is verified. / Master of Science

Green Stormwater Infrastructure (GSI) has become a popular method for flood mitigation as it can prevent runoff from entering streams during heavy precipitation. In this study, a recently developed neighborhood in Gresham, Oregon hosts a comparison of various GSI projects on runoff dynamics. The study site includes dispersed GSI (rain gardens, retention chambers, green streets) and centralized GSI (bioswales, detention ponds, detention pipes). For the 2017-2018 water year, hourly rainfall and observed discharge data is used to calibrate the EPA's Stormwater Management Model to simulate rainfall-runoff dynamics, achieving a Nash-Sutcliffe efficiency of 0.75 and Probability Bias statistic of 3.3%. A synthetic scenario analysis quantifies the impact of the study site GSI and compares dispersed and centralized arrangements. Each test was performed under four precipitation scenarios (of differing intensity and duration) for four metrics: runoff ratio, peak discharge, lag time, and flashiness. Design structure has significant impacts, reducing runoff ratio 10 to 20%, reducing peak discharge 26 to 68%, and reducing flashiness index 56 to 70%. There was a reverse impact on lag time, increasing it to 50 to 80%. Distributed GSI outperform centralized structures for all metrics, reducing runoff ratio 22 to 32%, reducing peak discharge 67 to 69%, increasing lag time 133 to 500%, and reducing flashiness index between 32 and 62%. This research serves as a basis for researchers and stormwater managers to understand potential impact of GSI on reducing runoff and downstream flooding in small urban watersheds with frequent rain.

published_or_final_version / Civil Engineering / Master / Master of Philosophy

Project Completion Report, OWRT Project No. A-086-ARIZ / Agreement No. 14-34-0001-8003, Project Dates: 10/01/77-9/30/78 / Acknowledgement: The work upon which this report is based was supported by funds provided by the State of Arizona and the United States Department of Interior, Office of Water Research and Technology as authorized under the Water Resources Act of 1964. / From the Introduction: "The research work under this contract has been conducted by graduate student David B. Dunbar and summarized in his M.S. thesis entitled "Analysis of a Parameter Estimation Technique for Linear Hydrologic Systems Using Monte Carlo Simulation" submitted to the Department of Hydrology and Water Resources, University of Arizona, Tucson, in 1981. The present report is a brief summary of Mr. Dunbar's thesis." David Dunbar's thesis is available at: http://arizona.openrepository.com/arizona/handle/10150/191728 / The primary accomplishment of this research has been demonstrating the power of the deconvolution technique developed by Neuman and de Marsily (1976) in dealing with noisy rainfall- runoff records of short duration. Such records are encountered in arid environments where rainfall often occurs in short isolated bursts and the data are measured with a considerable margin of error. Our research work consisted of superimposing known noise on synthetic rainfall- runoff data and examining the ability of the Neuman -de Marsily deconvolution method to estimate the correct impulse response of the system when the data include only a single storm event. Approximately 50 Monte Carlo simulation runs were performed for each of three different noise models considered in our work. The results clearly demonstrated that the deconvolution model leads to reliable estimates and can be used with confidence in the presence of realistic noise levels. In addition to the Monte Carlo simulation tests and their analysis, certain improvements were introduced into the original deconvolution technique. In particular, the original version of the technique required that the hydrologist exercise subjective judgement in choosing the "best" solution for the deconvolution problem from a large number of admissible solutions. Our new method of selecting the "best" result is based on a comparative analysis of residuals and is more reliable than the earlier subjective approach. The improved method has been applied to real as well as synthetic rainfall -runoff data.

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